1. Field of the Invention
This invention relates to analysis methodology and multivariate classification of diagnostic spectra, and, in particular, to methods for processing in vivo skin auto-fluorescence spectra for determining blood glucose levels. The invention also relates to methods of classifying cells or tissue samples or quantifying a component of a cell or tissue using a multivariate classification or quantification model.
2. Description of the Background
Near-IR spectra taken from agricultural samples, such as grains, oil, seeds and feeds, etc., have been used to quantitate various bulk constituents, e.g., total protein, water content, or fat content. See, P. Williams et al., “Agricultural Applications of Near-IR Spectroscopy and PLS Processing,” Canadian Grain Commission.
Multivariate statistical methods have long been used in the analysis of biomedical samples by infrared and near infrared, generally under the name “chemometrics.” See, U.S. Pat. No. 5,596,992 to Haaland et al., titled “Multivariate Classification of Infrared Spectra of Cell and Tissue Samples,” and U.S. Pat. No. 5,857,462 to Thomas et al., titled “Systematic Wavelength Selection For Improved Multivariate Spectral Analysis.”
The use of multivariate methods for the analysis of ex vivo tissue samples is well established. For spectra taken in vivo, some work has been done. Linear discriminant analysis has been used to classify visible/near-IR spectra of human finger joints into early and late rheumatoid arthritis classes. Multivariate methods have been used to classify fluorescence spectra taken in vivo from cervixes according to the presence or absence of cervical cancer or pre-cancerous tissues.
In general, the field of chemometrics is well established, and the use of multivariate statistical methods for the analysis of complex spectra is common. These methods are used in pharmaceutical analysis, industrial applications, and, more recently, biomedical spectral analysis.